(iii) \(\text{\textit {MHS}Atten}\)  Encoder: In [1]}, the authors showed that the attention mechanism allows a model to present global information in a given input sequence. Furthermore, attention-based architectures [2]}, [3]}, [4]}, [5]}, [6]} have shown promising performance in the context of sEMG-based HGR by extracting particular bits of information from the sequential nature of the sEMG signals. However, most of these models are still heavy-weight to be used in resource-constrained devices. Hence, in the proposed \(\text{HDCAM}\)  architecture, we designed a hybrid architecture that combines convolutions and attention mechanism advantages. Specifically, due to spatial inductive biases in convolution operation, the CNN-based encoder (\(\text{\textit {HD}Conv}\) ) assists our hybrid model to learn local representations with fewer parameters than solely attention-based models. However, to effectively
learn global representations, we also used an attention-based encoder (\(\text{\textit {MHS}Atten}\) ). Since computation in the MHA has quadratic relation to input size, we only used the \(\text{\textit {MHS}Atten}\)  encoder in the second and third stages of the \(\text{HDCAM}\)  to efficiently encode the global representation, where the length of the sequential feature maps are \(1/20\)  and \(1/40\)  of the original input of the network, respectively. The \(\text{\textit {MHS}Atten}\)  encoder can be represented as follows
